Abstract
The relation extraction of railway safety risk is important in constructing railway knowledge graph, which drives the development of intelligent transportation system through knowledge. However, existing models always concentrate on extracting relation from the universal text, fail to mine the railway text, especially the Chinese railway text. In this paper, we propose a novel relation extraction model named Transformer-aware Graph Convolution Networks (TGCN), which aims to comprehensively perceive railway semantics and improves the final results. Specifically, we construct a graph structure by the dependency tree to capture syntax features of input sentence, which can deep exploit the features contained in the sentence without introducing extra knowledge. In addition, we integrate these syntax features with contextual feature captured by the transformer mechanism. To better aggregate the railway semantic features, we use GCN to further encode the features confused by syntax and contextual features. Finally, a softmax classifier is used to identify the relation types. Extensive experiments on two real-world datasets show that, the proposed TGCN model achieves the state-of-the-art results. The TGCN provides a novel view to operate the railway safety risk management.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Wang, B., Wang, G., Wang, Y., Lou, Z., Hu, S., Ye, Y.: A K-means clustering method with feature learning for unbalanced vehicle fault diagnosis. Smart and Resilient Transp. 3(2), 162–176 (2021)
Zhang, P., Zheng, J., Lin, H., Liu, C., Zhao, Z., Li, C.: Vehicle trajectory data mining for artificial intelligence and real-time traffic information extraction. IEEE Trans. Intell. Transp. Syst. 1–11 (2023)
Liu, C., Yang, S.: Using text mining to establish knowledge graph from accident/incident reports in risk assessment. Expert Syst. Appl. 207, 117991 (2022)
Wu, S., He, Y.: Enriching pre-trained language model with entity information for relation classification. In: Proceedings of the 28th ACM international conference on information and knowledge management, pp. 2361–2364. ACM, Beijing, China (2019)
Zhao, K., Xu, H., Cheng, Y., Li, X., Gao, K.: Representation iterative fusion based on heterogeneous graph neural network for joint entity and relation extraction. Knowl.-Based Syst. 106888 (2021)
Devlin, J., Chang, M. W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 conference of the north American chapter of the association for computational linguistics: human language technologies, pp. 4171–4186. ACL, Minneapolis, MN, USA (2019)
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Adv. Neural Inf. Process. Syst. 5998–6008 (2017)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: 5th international conference on learning representations, OpenReview.net, Toulon, France (2017)
Geng, Z., Li, J., Han, Y., Zhang, Y.: Novel target attention convolutional neural network for relation classification. Inf. Sci. 24–37 (2022)
Li, Y., Long, G., Shen, T., Zhou, T., Yao, L., Huo, H., Jiang, J.: Self-attention enhanced selective gate with entity-aware embedding for distantly supervised relation extraction. In: Proceedings of the AAAI conference on artificial intelligence, pp. 8269–8276. AAAI, New York, NY, USA (2020)
Veyseh, A., Dernoncourt, F., Thai, M., Dou, D., Nguyen, T.: Multi-view consistency for relation extraction via mutual information and structure prediction. In: Proceedings of the AAAI conference on artificial intelligence, pp. 9106–9113. AAAI, New York, NY, USA (2020)
Tian, Y., Chen, G., Song, Y., Wan, X.: Dependency-driven relation extraction with attentive graph convolutional networks. In: Proceedings of the 59th annual meeting of the association for computational linguistics, pp. 4458–4471. ACL, Virtual Event (2021)
Qiu, B., Fan, W.: Travel time forecasting on a freeway corridor: a dynamic information fusion model based on the random forests approach. Smart Resilient Transp. 3(2), 131–148 (2021)
Zhou, T., Zhang, H., Ai, B., Xue, C., Liu, L.: Deep-learning-based spatial–temporal channel prediction for smart high-speed railway communication networks. IEEE Trans. Wirel. Commun. 5333–5345 (2022)
Du, H., Teng, S., Chen, H., Ma, J., Wang, X., Gou, C., Li, B., Ma, S., Miao, Q., Na, X., Ye, P., Wang, F.Y.: Chat with chatgpt on intelligent vehicles: an IEEE TIV perspective. IEEE Trans. Intell. Vehicles 8(3), 2020–2026 (2023)
Acknowledgments
This work is supported by the National Natural Science Foundation of China under Grant No.62176239, Henan Province Key Research and Development Promotion Projects (Key Scientific and Technological Problems) under Grant No.212102210548.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 Beijing Paike Culture Commu. Co., Ltd.
About this paper
Cite this paper
Wang, Y., Li, X., Wu, Y., She, W., Ye, Y. (2024). Transformer-Aware Graph Convolution Networks for Relation Extraction of Railway Safety Risk. In: Gong, M., Jia, L., Qin, Y., Yang, J., Liu, Z., An, M. (eds) Proceedings of the 6th International Conference on Electrical Engineering and Information Technologies for Rail Transportation (EITRT) 2023. EITRT 2023. Lecture Notes in Electrical Engineering, vol 1138. Springer, Singapore. https://doi.org/10.1007/978-981-99-9319-2_16
Download citation
DOI: https://doi.org/10.1007/978-981-99-9319-2_16
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-9318-5
Online ISBN: 978-981-99-9319-2
eBook Packages: EngineeringEngineering (R0)